Demand Response

According to Federal Energy Regulatory Commission, Demand Response (DR) is defined as [10]:

“Changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments de-signed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.”

According to U.S Department of Energy demand response is defined as [11]:

“a customer’s opportunity either to reduce or shift their electricity usage during peak pe-riods in response to time-based rates or other forms of financial incentives”.

2.1.1 Demand Response Classification

Based on how the residential and industrial facilities change their electric usage, Depart-ment of Energy (DOE) has divided the DR program as show in the Figure 1:

Figure 1. Classification of DR program [10], [12], [13]

2.1.1.1 Incentive-based DR program

From Figure 1, the Electricity Consumers (EC’s) participating in dispatchable or incentive demand response program allows the facility operators to monitor and control their con-sumption pattern. For controlling the facilities electric devices, either Direct Load Control (DLC) program or Interruptible/Curtailable (I/C) service are used.

A participation payment is provided by the system operators to the consumers who are enrolled in DLC program. The consumer may override the DLC program with reduced incentive payment. [14] demonstrates a real-time DLC incentive-based DR program with home energy management system. Three simulated scenarios and EC’s financial benefits were compared. The DLC approach is less considered by the EC’s because of their elec-tricity consumption privacy.

The EC’s pay penalties if they override during I/C service and Capacity Market Programs (CMP) are provided to EC’s when they curtail their load when grid contingencies arise [13]. An economic model was proposed in [15] for I/C and CMP program. The ISO uses the result of the model to study the behavior of the EC’s corresponding to the incentives and penalties provided by the electricity suppliers.

Ancillary DR service allows the EC’s as operating reserves. The consumers load curtail-ment bid are placed in ISO/RTO spot market. If the ISO/RTO accepts the consumers and their bids, the electricity market value is paid to them on participation in the DR program.

[16] explains the electricity market policies and barriers to DR ancillary service.

During the reliability-triggered events the Emergency DR program (EDRP) are activated.

An innovative Analytic Hierarchy Process (AHP) is proposed by the author in [17] to de-sign EDRP. Incentives are provided to the EC’s for measured load curtailment during those events. Unlike I/C service the EC’s may or may not levy penalties if they override EDRP.

The Demand Bidding/Buyback (DB) program allows the consumers to bid directly in the wholesale electricity markets [13]. The impact between DB program and Market Clearing Price (MCP) is analyzed in [18]. The analysis from the model shows EC’s profit, reduction in MCP and electricity generation cost.

2.1.1.2 Time-based DR program

From Figure 1, the Non-dispatchable or Price-Based demand response programs (PBP), the price of electricity fluctuates depending on the electricity production. In general, the Time of Use (TOU), Critical Peak Pricing (CPP), and Real-Time Pricing (RTP) are PBP’s dynamic price programs. A higher electricity price is offered to EC’s to reduce the demand during the on-peak period and lower price is offered during off-peak periods.

In Time-of-use DR program the 24-hour day is divided into different block of times. The rates are pre-determined months or years ahead which reflects the average electricity pro-duction and distributing cost [13]. Modeling DR program by TOU program is described in [19]. The [20] and [21] studies carried on residential EC’s shows the ineffectiveness of TOU demand response program.

The utility at the earliest provides pre-specified electricity rates to EC’s. The pre-specified duration will have the CPP which are very high. However, the EC’s receives discount dur-ing non-CPP periods [13]. CPP demand response is not widely used as a DR strategy be-cause of the fluctuation in the daily electricity market.

The EC’s receives price of electricity a day-ahead in RTP or Hourly Price (HP) program [13]. The results from [22] argues that Hourly Price DR program engages the EC’s more efficiently in Demand Side Management.

2.1.2 Demand Response Benefits

The following Table 1 describes DR benefits. From [13] and [23] the efficiency achieved by DR program is categorized into three types:

Types of Benefits Recipient Description

Financial benefits EC’s participating in DR pro-gram

(1) Electricity bill saving. (2) Incentive payment from utili-ties. (3) Indirect financial ben-efits by reducing congestion in the electric grid.

Electricity Market (1) Because of efficient use of resource and electric system, the electricity supply cost is reduced. (2) Indirectly re-duces in building additional production, transmission and distribution lines.

Reliability benefit Some or all EC’s The forced outages are re-duced for all the electricity consumers and the grid relia-bility is maintained by differ-ent energy sources.

Environmental and Other benefits

Some or all EC’s ISO/RTO

(1) More innovation in retail energy market. (2) Electric consumption reduction and emission deduction in high-polluting manufacturing facili-ties in peak-hours

Table 1. Demand Response program benefits

2.1.3 Demand Response in Wholesale Electricity market

It is essential to have the knowledge of wholesale electricity market structure to understand Energy market DR program (EMDR). Figure 2 shows the participants in wholesale elec-tricity market. The generation, transmission and distribution companies can be grouped together as energy suppliers. The wholesale electricity market exists when the energy sup-pliers compete among themselves while offering electricity to the retailers. The retailers who are Independent System Operator (ISO) or Regional Transmission Organization (RTO) re-structure the electricity market price when selling to the consumers.

Figure 2. Electricity Market Structure [24]

The Distributed Energy Resource (DER) Aggregators are group of companies who buys electricity from the retailers at a negotiable price.

Different types of wholesale electricity market (Ancillary Service Market, Real-Time Im-balance Market, Transmission, Hour Ahead Market and Day Ahead Market) is discussed in [24]. However, as mentioned in Chapter 1.4 only the Day Ahead Market is considered in constructing the communication infrastructure for DR program in this thesis.

2.1.4 Day Ahead Market

As shown in Figure 3, Day Ahead Market is a category of electricity spot market.

Figure 3. Types of spot market in wholesale electricity market [25]

In Day ahead market, the retailers buy and sell the electricity from the energy suppliers to the EC’s a day ahead or 24 hours ahead [24]. The 24 hour is divided into hour by hour and each one hour have different electricity price.

As mentioned in [26] the hourly price of electricity for the next day is announced by the utilities after noon or later. Figure 4 gives an overview of distributed electricity price over day ahead hours.

Figure 4. Day ahead electricity price

2.1.5 DR program Participation

As shown in Figure5, the EC’s can engage in the Demand Response program by one of the following ways [27],

Consumers Provisioned Model – The EC’s directly participate in the DR program with their on-premises available infrastructure.

Traditional Utility Model – The EC’s sign-up to with the utilities. The utilities provide the infrastructure and the day ahead market price to the EC’s to participate in the DR program.

Third-Party Aggregators – Two or more party specialized in DR participation aggregate and form third-party Demand Side Response Aggregator (DSRA). The DSRA make their own policies with the EC’s and provides the DR program to System Operators.

Figure 5. EC’s participation in DR program

Based on the business model [28] and information model the DR program participants are shown in Figure 6.

Figure 6. DR Participants

The Grid Operators are electricity suppliers and they decide the wholesale electricity mar-ket. The Utilities are composed of the DR service providers who monitors the EC’s elec-tricity usage. They alert the EC’s when the grid is unstable or during the high electric price depending the EC’s demand response enrollment. The Facilities are actual Electricity Con-sumers who gets incentives and participate in the DR program.

2.1.6 State of Art Demand Response Participant Modeling

The article [29] examines a Simulated Demand Response of a Residential Energy Man-agement System (REMS). The proposed system is a discrete event system that can able to monitor, plan and control the energy consumption in residential buildings. The authors in the article proposed “components” to be used for the REMS system. The components in-clude actors and equipment. Actors were divided into primary (Owner, utility and REMS

Controller) and secondary (dishwasher, plug-in-electric vehicle, water heater, and house-hold equipment). According to the authors, the primary actors initiate the system events and the secondary actors respond to those events. A user interface was designed to the end user to monitor the energy consumption and to override the equipment status. The authors analyzed the residential appliance electricity demand and their operating duration. There are mainly two functions provided by the REMS system. First, the system delays the equip-ment that was not utilized at the time of Demand Response service time. Second, during the Demand Response service time, energy dissipates from the stationary battery to the equipment. The authors used four scenarios to define the REMS system. The first scenario was constructed with the normal electricity consumption without any programmable ther-mostat. The next scenario integrates the programmable thermostat to the normal electricity consumption. The third was constructed with the REMS and the equipment’s, and the final scenario with the REMS and a stationary battery. The authors conclude from the simulated result that, the REMS can provide an energy consumption reduction during the DR Service time. Furthermore, cost-benefit analysis was carried out by the authors that showed pay-back period for the consumers by participating in the Demand Response program.

The article [30], proposes a model of Demand Response (DR) Energy Management System in the Industrial Facilities. The effects and the importance of Demand Response in Indus-trial sectors were discussed by the authors. To have a common understanding of DR pro-gram, a model element and a model architecture were defined by the authors. Initially each model elements were identified by unique graphical symbols. Later the authors labelled each model element. Finally, they constructed an architectural overview representing each model elements and their interrelationship. The authors modelled the production flow as a State-Task Network (STN) that defines two types of nodes. A “state node” that represents the inputs, intermediate products, and the final products; and a task node that represents the manufacturing process. The authors further classified the task nodes into Scheduling task (process that can be scheduled prior with respect to electricity demand) and Non-Scheduling task (process that cannot be scheduled prior with respect to electricity demand).

The authors finally described the developed model with the help of a steel manufacturing facilities.

A series of DR messages exchanged between the utility and facility side corresponding to dynamic real-time electricity price was provided by the authors.

Figure 7. Model of DR program in industrial facility [30]

Using the model elements and the model architecture, the authors were able to construct the pictorial representation of the demand response program in the steel manufacturing facility.

The authors [31] in proposes production planning model with Time Of Use DR program in air separation plant and cement plant industrial facilities. The authors of [32] discussed various smart grid technologies and presented case studies of ancillary service and food processing industrial facility participating in DR program.

The difference between old and new electricity market is discussed in reference [33]. [6]

and [36]outlines the problem in implementing DR program and emphasis the need of DR architecture. Furthermore references [35]–[38] describes more on DR concepts and mod-els. Even though the references explain the modeling of DR participants, the interaction between the participants were not studied.